TY - GEN
T1 - Dynamic neural networks applied to melody retrieval
AU - Gomez, Laura E.
AU - Sossa, Humberto
AU - Barron, Ricardo
AU - Jimenez, Julio F.
N1 - Funding Information:
We wish to thank the Centro de Investigación en Computación of the I.P.N. by the support to accomplish this project. L.E. Gomez and J.F. Jimenez thank CONACYT by the scholarship received to complete their doctoral studies. R. Barron thanks the SIP-IPN under grant 20100379 for the support. H. Sossa thanks the SIP-IPN under grant 20100468 for the support. Authors thank the European Union, the European Commission and CONACYT for the economical support. This paper has been prepared by economical support of the European Commission under grant FONCICYT93829. The content of this paper is an exclusive responsibility of the CIC-IPN and it cannot be considered that it reflects the position of the European Union. Finally, authors thank the reviewers for their comments for the improvement of this paper.
Funding Information:
Acknowledgements. We wish to thank the Centro de Investigación en Computación of the I.P.N. by the support to accomplish this project. L.E. Gomez and J.F. Jimenez thank CONACYT by the scholarship received to complete their doctoral studies. R. Barron thanks the SIP-IPN under grant 20100379 for the support. H. Sossa thanks the SIP-IPN under grant 20100468 for the support. Authors thank the European Union, the European Commission and CONACYT for the economical support. This paper has been prepared by economical support of the European Commission under grant FONCICYT 93829. The content of this paper is an exclusive responsibility of the CIC-IPN and it cannot be considered that it reflects the position of the European Union. Finally, authors thank the reviewers for their comments for the improvement of this paper.
PY - 2010
Y1 - 2010
N2 - A new method for the retrieval of melodies from a database is described in this paper. For its functioning, the method makes use of Dynamic Neural Networks (DNN). During training a set ofDNN is first trained with information of the melodies to be retrieved. Instead of using traditional signal descriptors we use the matrix of synaptic weights that can be efficiently used for melody representation and retrieval. Most of the reported works have been focused on the symbolic representation of musical information. None of them have provided good results with original signals.
AB - A new method for the retrieval of melodies from a database is described in this paper. For its functioning, the method makes use of Dynamic Neural Networks (DNN). During training a set ofDNN is first trained with information of the melodies to be retrieved. Instead of using traditional signal descriptors we use the matrix of synaptic weights that can be efficiently used for melody representation and retrieval. Most of the reported works have been focused on the symbolic representation of musical information. None of them have provided good results with original signals.
KW - Dynamic Neuronal Networks
KW - Music Information Retrieval
KW - Musical Descriptors
UR - http://www.scopus.com/inward/record.url?scp=78650009502&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-16773-7_23
DO - 10.1007/978-3-642-16773-7_23
M3 - Contribución a la conferencia
SN - 3642167721
SN - 9783642167720
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 269
EP - 279
BT - Advances in Soft Computing - 9th Mexican International Conference on Artificial Intelligence, MICAI 2010, Proceedings
T2 - 9th Mexican International Conference on Artificial Intelligence, MICAI 2010
Y2 - 8 November 2010 through 13 November 2010
ER -